interlaboratory reproducibility of blood6 journal of laboratory automation manual differentials for...

Journal of Laboratory Automation 1 –6© 2015 Society for LaboratoryAutomation and ScreeningDOI: 10.1177/2211068215584278jala.sagepub.com

Technology Brief

Introduction

Morphological analysis of blood cells is invaluable to patient management by the clinician. Until now, manual morphological assessment using the microscope has been set as the gold standard. However, manual assessment of a blood smear is subject to individual interpretation of images, resulting in significant interobserver variability.1–3 In addi-tion, correct morphological classification is labor-intensive and requires continuous training of laboratory personnel. Automated digital morphological assessment of blood cells is therefore considered a valuable development, as it can overcome these drawbacks. The digital microscope (DM) offers several advantages. First, the DM ensures the con-stant presence of a morphological expert system in the rou-tine laboratory. Second, the system stores an image of every analyzed cell, thereby offering the ability to re-evaluate cell types with colleagues and other pathology experts, either directly or by using telehematology.3–6 Finally, the system enables us to digitally archive blood smear and body fluid samples indefinitely.

Since the 1970s, several automated image processing devices have been developed by various manufacturers.7 It was previously shown that a DM system, using several advanced mathematical algorithms, is capable of correct

classification of leukocytes in peripheral blood and body fluid samples in relation to manual microscopic assessment of the five main peripheral blood cell categories.3–5,8–10 An overall accuracy of 92.0% was found when the preclassifi-cation results of the DM96 (Cellavision, Lund, Sweden) were compared to those of manual assessment.3,11 It has been shown that the classification performance of this par-ticular system is as reliable as manual classification by

584278 JLAXXX10.1177/2211068215584278Journal of Laboratory AutomationRiedl et al.research-article2015

1Department of Clinical Chemistry, Geïntegreerd Klinisch Chemisch Laboratorium, Albert Schweitzer Hospital, Dordrecht, Netherlands2Department of Internal Medicine, Albert Schweitzer Hospital, Dordrecht, Netherlands3Department of Clinical Chemistry, Vlietland Hospital, Schiedam, Netherlands4Department of Clinical Chemistry, Erasmus Medical Centre, Rotterdam, Netherlands*Department of Clinical Chemistry, IJsselland Hospital, Capelle aan den IJssel, Netherlands

Received Sep 9, 2014.

Corresponding Author:Jurgen A. Riedl, PhD, Department of Clinical Chemistry and Haematology, Albert Schweitzer Hospital, P.O. Box 444, 3300 AK Dordrecht, Netherlands. Email: [email protected]

Interlaboratory Reproducibility of Blood Morphology Using the Digital Microscope

Jurgen A. Riedl1, Karlijn Stouten1,2, Huib Ceelie3, Joke Boonstra4*, Mark-David Levin2, and Warry van Gelder1

AbstractDifferential counting of peripheral blood cells is an important diagnostic tool. However, manual morphological analysis using the microscope is time-consuming and requires highly trained personnel. The digital microscope is capable of performing an automated peripheral blood cell differential, which is as reliable as manual classification by experienced laboratory technicians. To date, information concerning the interlaboratory variation and quality of cell classification by independently operated digital microscopy systems is limited. We compared four independently operated digital microscope systems for their ability in classifying the five main peripheral blood cell classes and detection of blast cells in 200 randomly selected samples. Set against the averaged results, the R2 values for neutrophils ranged between 0.90 and 0.96, for lymphocytes between 0.83 and 0.94, for monocytes between 0.77 and 0.82, for eosinophils between 0.70 and 0.78, and for blast cells between 0.94 and 0.99. The R2 values for the basophils were between 0.28 and 0.34. This study shows that independently operated digital microscopy systems yield reproducible preclassification results when determining the percentages of neutrophils, eosinophils, lymphocytes, monocytes, and blast cells in a peripheral blood smear. Detection of basophils was hampered by the low incidence of this cell class in the samples.

Keywordsblood differential, hematology, digital microscopy, preclassification

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2 Journal of Laboratory Automation

experienced laboratory technicians in classifying the five main peripheral blood cell categories.3

Only limited information is currently available regarding interlaboratory variation and quality in cell classification by independently operated digital microscopy systems. As part of the continuing validation of DM systems, it is important to assess the interlaboratory variation between systems operated at different locations and determine whether they can produce comparable results. To achieve this, we com-pared four independently operated DM systems when ana-lyzing randomly selected samples.

Materials and Methods

Digital Microscope Systems and Locations

In this study we set out to compare four independently oper-ated digital microscope systems (DM96) for their ability to classify the five main peripheral blood cell classes (neutro-phils, lymphocytes, monocytes, eosinophils, and basophils) and blast cells in 200 samples. The DM machines were located at four different clinical chemistry laboratories in the Netherlands: the Albert Schweitzer Hospital (ASz), the Vlietland Hospital (Vlietland), the Erasmus Medical Centre, central location (Centrum), and the Erasmus Medical Centre Daniel den Hoed Cancer Clinic (Daniel).

Blood Sample Collection and Analysis

Using standardized protocols, each laboratory collected peripheral blood samples from 50 randomly selected patients and generated four blood smear specimens per sample. Each hospital location received one smear speci-men from each patient. A total of 200 specimens were ana-lyzed at each location. Prior to sample analysis, all four DM systems were calibrated using a calibration slide. In addi-tion, each system was set to analyze and classify 200 leuko-cytes per sample. At each location the samples were processed on the DM by two local technicians following standardized procedures. This study focused on the preclas-sification results obtained from the four different DM sys-tems. Preclassification is defined as the initial classification performed by the DM, without intervention or correction by

the local operator. Therefore, results could not be influ-enced by manual interference.

Interlaboratory Variation

The number of each cell type found (neutrophils, lympho-cytes, monocytes, eosinophils, basophils, and blast cells) was expressed as a percentage of the total amount of cells classified. For each location, the individual preclassifica-tion result per cell class was compared to the averaged per-centage of the other three locations in order to determine the interlaboratory variation.

Statistics

The coefficient of determination (R2) was calculated for each comparison in order to determine the interlaboratory variation, using Statistical Package for the Social Sciences (SPSS) version 18 for Windows.

Results and Discussion

The total number of classified leukocytes did not reach 200 cells in all samples. The range in numbers and percentages per cell class per location is shown in Table 1. Figure 1a–f shows scatter plots and the associated R2 value for each com-parison per cell class. Overall, small interlaboratory variation was found for neutrophils (R2 = 0.90–0.96), lymphocytes (R2 = 0.83–0.94), monocytes (R2 = 0.77–0.82), eosinophils (R2 = 0.70–0.78), and blast cells (R2 = 0.94–0.99). Only baso-phils showed a large variation (R2 = 0.28–0.34).

As part of the continuing validation of DM systems for morphological analysis of a peripheral blood smear, the interlaboratory variation for the five main blood cell classes and blast cells was determined. This is the first published study that considers this variation between independently operated digital microscopy systems. The preclassification results show small interlaboratory variation for four of the five main peripheral blood cell classes. This is comparable to R2 values found when comparing two manual differential counts, as done by Ceelie et al.3 The DM showed even less variation between several machines than between the two

Table 1. Ranges for Each Cell Class Found at the Different Locations (ASz, Centrum, Daniel, and Vlietland).

ASz Centrum Daniel Vlietland

# % # % # % # %

Neutrophils 2–189 1.1–96.9 0–192 0.0–97.0 0–191 0.0–97.4 0–190 0.0–96.9Lymphocytes 0–184 0.0–92.4 1–179 0.5–92.7 2–187 1.0–93.5 1–178 0.5–90.8Monocytes 1–73 0.5–38.2 1–65 0.5–35.7 0–75 0.0–39.3 0–75 0.0–40.8Eosinophils 0–32 0.0–16.0 0–26 0.0–20.7 0–40 0.0–21.3 0–56 0.0–28.4Basophils 0–12 0.0–7.7 0–11 0.0–6.0 0–10 0.0–6.5 0–10 0.0–5.4Blast cells 0–151 0.0–82.1 0–137 0.0–86.2 0–167 0.0–88.4 0–123 0.0–73.7



Riedl et al. 3

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Riedl et al. 5

Figure 1. Results of preclassification comparisons for segmented neutrophils (a), lymphocytes (b), monocytes (c), eosinophils (d), basophils (e), and blast cells (f). The Y axis shows the percentage of the cell classes found in each of the 200 samples per location (Vlietland, ASz, Daniel, and Centrum). The X axis shows the average percentage of the difference cell classes found at the four locations excluding the location on the Y axis.




manual differentials for neutrophils (R2 = 0.90 for manual count) and monocytes (R2 = 0.65 for manual count).3

Blast cells were detected with an excellent accuracy, despite the fact that the overall average percentage of blast cells was low. This is probably due to the large spread in counted cells per sample. Not every sample contained blast cells, which lowers the overall average percentage. The same was observed when two manual counts of blast cells were compared, resulting in an R2 value of 0.97.3 Again, the DM showed even less variation between systems than between experienced morphologists, since R2 values between 0.94 and 0.99 were found in this study.

Only the preclassification results of the basophils showed considerable interlaboratory variation. This variation was also seen when comparing manual assessment by an experi-enced morphologist to a reference differential, as done by Briggs et al.4 Even an experienced morphologist could not achieve an R2 value higher than 0.30 when manually clas-sifying basophils. Briggs et al.4 also compared the manual differentials executed by two experienced morphologists with the DM. This resulted in an R2 value of 0.00.4 Similar results were found by Ceelie et al.3 when comparing two manual differential counts with each other and with the DM. The poor R2 values for basophils are due to the low number of detected cells of this class per peripheral blood smear, leading to profound relative differences in detected percentages of basophils at different locations.

A database containing approximately 1.4 million leuko-cytes was set up to compare the preclassification performance of the DM with the manual assessment of peripheral blood smears by experienced morphologists. This database yielded an R2 value of 0.88 for the basophils (Riedl, data not yet pub-lished). The size of that database overcomes the problem encountered in this study, which was hampered by the low number of counted basophils in the various samples. The same was observed in a study by Lee et al.,12 who did not include normal blood smears and therefore may have had more baso-phils than is usually observed. Their comparison between the DM96 and a manual count gave an R2 value of 0.76.12

In conclusion, this study shows that independently oper-ated digital microscopy systems, stationed at four different locations, yield reproducible preclassification results when determining percentages of neutrophils, lymphocytes, monocytes, and eosinophils present in a blood smear. In addition, blast cells were also detected correctly and with only minor variation in detected percentages between the different microscopy systems. The classification of baso-phils was less accurate because of the low number of baso-phils present in these samples.

ContributorshipJAR, WG, JB, and KS: experimental design setup and writing of the manuscript. HC and MDL: affiliated researchers. All authors read and approved the final manuscript.

Acknowledgement

We thank Rebecca Buis for critical reading of the manuscript.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, author-ship, and/or publication of this article.

References

1. Rumke, C. L. Imprecision of Ratio-Derived Differential Leukocyte Counts. Blood Cells 1985, 11, 311–314.

2. Bentley, S. A. Automated Differential White Cell Counts: A Critical Appraisal. Baillieres Clin. Haematol., 1990, 3, 851–869.

3. Ceelie, H.; Dinkelaar, R. B.; van Gelder, W. Examination of Peripheral Blood Films Using Automated Microscopy: Evaluation of Diffmaster Octavia and Cellavision DM96. J. Clin. Pathol., 2007, 60, 72–79.

4. Briggs, C.; Longair, I.; Slavik, M.; et al. Can Automated Blood Film Analysis Replace the Manual Differential? An Evaluation of the Cellavision DM96 Automated Image Analysis System. Int. J. Lab. Hematol., 2009, 31, 48–60.

5. Kratz, A.; Bengtsson, H. I.; Casey, J. E.; et al. Performance Evaluation of the Cellavision DM96 System: WBC Differentials by Automated Digital Image Analysis Supported by an Artificial Neural Network. Am. J. Clin. Pathol., 2005, 124, 770–781.

6. Billard, M.; Lainey, E.; Armoogum, P.; et al. Evaluation of the CellaVision™ DM Automated Microscope in Pediatrics. Int. J. Lab. Hematol., 2010, 32, 530–538.

7. Tatsumi, N.; Pierre, R. V. Automated Image Processing: Past, Present, and Future of Blood Cell Morphology Identification. Clin. Lab. Med., 2002, 22, 299–315.

8. Riedl, J. A.; Dinkelaar, R. B.; van Gelder, W. Automated Morphological Analysis of Cells in Body Fluids by the Digital Microscopy System DM96. J. Clin. Pathol., 2010, 63, 538–543.

9. Swolin, B.; Simonsson, P.; Backman, S.; et al. Differential Counting of Blood Leukocytes Using Automated Microscopy and a Decision Support System Based on Artificial Neural Networks: Evaluation of DiffMaster™ Octavia. Clin. Lab. Haematol., 2003, 25, 139–147.

10. Rollins-Raval, M. A.; Raval, J. S.; Contis, L. Experience with CellaVision DM96 for Peripheral Blood Differentials in a Large Multi-Center Academic Hospital System. J. Pathol. Inform. 2012, 3, 29.

11. Seeto, M.; Ledesma, A.; Duff, J. Evaluation of the CellaVision DM96 System: Familiarisation Study in a Large Laboratory Setting. Int. J. Lab. Hematol., 2008, 30(Suppl. 1), 77.

12. Lee, L. H.; Mansoor, A.; Wood, B.; et al. Performance of CellaVision DM96 in Leukocyte Classification. J. Pathol. Inform., 2013, 4, 14.



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